Epidemiologist behind Imperial College coronavirus model revises model
dailywire.comThis is misinformation. This is not a major revision in any sense. Read the Imperial College paper. The estimates were different between the 'mitigation' and 'supression' strategy. The UK have now changed strategy to 'supression'. Hence the different prediction.
Now at the bottom of the article...
"Correction: The original title of this article incorrectly suggested that Neil Ferguson stated his initial model was wrong. The article has been revised to make clear that he provided a downgraded projection given the new data and current mitigation steps."
I've read the Imperial College Paper. Transmissibility is a major change [0]. The original paper says we are only just at the beginning of the time we will have to spend quarantined over the next 12-18 months, off and on. With the new transmissibility estimate, it would mean that we are a few weeks away from the peak and then done with this.
If you can show me I'm wrong, please do.
https://www.newscientist.com/article/2238578-uk-has-enough-i...
> Transmissibility is a major change [0]
I'm not sure you read your own link correctly.
"New data from the rest of Europe suggests that the outbreak is running faster than expected, said Ferguson. As a result, epidemiologists have revised their estimate of the reproduction number (R0) of the virus. This measure of how many other people a carrier usually infects is now believed to be just over three, he said, up from 2.5. “That adds more evidence to support the more intensive social distancing measures,” he said."
I'm sorry. You are correct. Although Ferguson attributes most of the benefit to the lockdown, he does acknowledge a change of R0 further revising the numbers down.
Where I got confused was attributing some of the things said by Gupta (behind the Oxford model) to Ferguson (behind the Imperial College model).
I don't know if you've played with the model, but going from 2.6 R0 to 3 is a significant difference, doubling the death rate in some cases. I am very curious how this affects the modeling. If the real world death rate is not corresponding to the model, then this could indicate a lower lethality and wider spread than previously anticipated.
This is my reading. I believe that this is correct.
They claim half the population is already infected. So conduct a random sample, test 50 people and you would expect 25ish MTO be positive, with most showing no symptoms. If so, then herd immunity is indeed a thing.
I have my doubts that the numbers would support this claim. And if so, then virtually everyone in Spain or Italy would already be a carrier.
The fact that cases were linked to known arrivals also is evidence against this hypothesis : if a high proportion of carriers were unwitting and asymptomatic you would expect many of those diagnosed to not have a link to someone previously diagnosed.
I agree with your doubts, and I can't make sense of 72% of tests in NY being negative.
However, the test you need to run is an antibody test, since negative tests don't tell you whether you've already had it.
Given the UK is now only testing those who turn up to a hospital and likely needing treatment and is still finding that about 15% of those are Sars-cov-19 50% in the wider population sounds exceptionally unlikely.
I'd really like to see this discussed here. I've not seen anybody talking about this in the US. This is the guy behind the "highly-cited Imperial College London coronavirus model."
And this is a major revision. It drops estimated deaths in the UK from 500,000 to "20,000 or far fewer." It also estimates that the UK will not run out of ICU beds in the process.
The reason is that the transmissibility estimate has gone up, which implies that many more people have already had the virus than we realized. This, in turn, means that a much lower percentage are serious cases. It also means that we are much nearer to the peak than we thought.
Edited to add: He also credits the lockdown in the UK, but if you look at the previous model of how this plays out even with a complete lockdown, you see that the vast majority of the change must come from the change in estimate of transmissibility.
> It also estimates that the UK will not run out of ICU beds in the process.
That'd be wonderful. But, doesn't that conflict with what we saw actually happen in Italy?
Not exactly.
Italy has never run out of ICU beds. There have been sporadic and garbled reports of temporary shortages in Lombardy specifically, alleviated by patient transfers and ward conversions, and some of those reports were contradictory (e.g. doctors or mayors saying they were rationing care but other more senior healthcare leaders saying they weren't).
The UK had a hospital that hit capacity temporarily but it only lasted 12 hours before transfers reduced the pressure again.
We'll be seeing a lot of activity like that in the next weeks - reports that hospitals are full, then they stop being full as more capacity is added or patients are rebalanced onto other hospitals. The assumption of total ICU exhaustion seems to have been based on the assumption of uniform case growth everywhere which isn't happening, and perhaps also an inability to quickly add capacity.
Not necessarily. If transmissibility is high, you could be seeing close to the peak.
The data out of NY that makes me most suspicious of this new model is actually the 72% negative rate on tests. I would expect that to be a lot lower. But that's just a gut feeling.
Half of those 72% may have had it three weeks ago and are a-ok now. Without testing for antibodies, all we can say is they do not _currently actively have an infection._
The tests have pretty high false negative rates. https://twitter.com/cynthiaccox/status/1242868006293843971/p...
It does indeed, or what we are seeing in New York. I'm very skeptical of this, though it would of course be great news if it was true. I can't help but have whiplash from the fact that the UK seems to have gone from an attitude of we're going to be fine, to we're all going to die, to now it's going to be fine again.
Edit: I think it is important to keep in mind that Italy's deaths per day are still going up. The growth has slowed and hopefully we are about to see the peak but it still hasn't come. Most western countries are on the same growth rate as Italy, there is no reason right now to think we won't see something similar happen all over the world.
The number of new case per day in Italy has stabilized at 5000 to 6000 per day.
Date Cases New Cases March 10 9172 1797 March 11 10149 977 March 12 12462 2313 March 13 15113 2651 March 14 17660 2547 March 15 21157 3497 March 16 24747 3590 March 17 27980 3233 March 18 31506 3526 March 19 35713 4207 March 20 41035 5322 March 21 47021 5986 March 22 53578 6557 March 23 59138 5560 March 24 63927 4789 March 25 69176 5249 March 26 74386 5210New cases isn't a good metric because of how testing can change. Deaths is more reliable metric to be used when prevalence is high, as it is in Italy and now the US.
And in the case of Italy, you would expect the new case rate to outpace itself for a long while as testing is ramped up, but even with increased testing the new case level is leveling off. This indicates an already infected populace that has since recovered.
deaths are not a reliable metric at all. old and sick people are far more susceptible.
New York is close to getting there, too.
Florence Italy was home to Hug A Chinese National Day during the Chinese New Year when many Chinese people are allowed to travel globally.
Hopefully fewer visited the UK and even fewer had close contact with the locals.
> Hug A Chinese National Day
A rational person can't possibly believe that this is the cause of their troubles, even if there were such a thing.
I agree it’s hard to believe such a thing existed and undoubtedly accelerated the spread of the Wuhan Virus. Many lives could have been saved if Italy embraced social distancing instead of virtue signalling.
Proof: https://www.westernjournal.com/italian-mayor-starts-hug-chin...
>The reason is that the transmissibility estimate has gone up
citation please.
My understanding is that it's because of the policy intervention that his team advised.
Ok - I read this :
"He said that expected increases in National Health Service capacity and ongoing restrictions to people’s movements make him “reasonably confident” the health service can cope when the predicted peak of the epidemic arrives in two or three weeks. UK deaths from the disease are now unlikely to exceed 20,000, he said, and could be much lower."
and
"This measure of how many other people a carrier usually infects is now believed to be just over three, he said, up from 2.5. “That adds more evidence to support the more intensive social distancing measures,” he said."
So the article says EXACTLY THE OPPOSITE OF WHAT YOU CLAIMED.
This is disinformation.
I've been mentioning this possibility here and getting in arguments over it for more than a week now. People don't have much of an appetite, it seems. My guess is that all the folks who were absolutely certain of a low R0 value and a high IFR, with 2% of the US population dying as a result, are not going to show up to acknowledge that they were wrong.
I always thought the Imperial model was ridiculously fatalistic, but this turnabout, and this quickly - that really surprised me.
the new model reads like a bunch of politically motivated bullshit. The model is saying whatever the current Tory government needs it to say and seems divorced from what we can actually see on the ground.
What’s the evidence for widespread transmission already?
I wish there was some. Again, it's based on the change in R0. From another article[0]:
"New data from the rest of Europe suggests that the outbreak is running faster than expected, said Ferguson. As a result, epidemiologists have revised their estimate of the reproduction number (R0) of the virus. This measure of how many other people a carrier usually infects is now believed to be just over three, he said, up from 2.5."
[0]: https://www.newscientist.com/article/2238578-uk-has-enough-i...
An R0 of 3 is still catastrophic. It’s your whole nuclear family plus one. What was R0 before?
Can't help but ask the actuary question: are the covid19 deaths avoided worth the economic cost of the lockdown?
read what the report says:
[...] coronavirus will probably kill under 20,000 people in the U.K. — more than 1/2 of whom would have died by the end of the year in any case [because] they were so old and sick [...]
Are you personally willing to die to get the DOW above 30K? If you're not it seems rude to ask others to.
It isn't about the Dow. It is about the restaurants that don't have cash-on-hand having to close. It is about the people who live paycheck-to-paycheck now having to decide how to spend their last twenty dollars with the only hope of relief being a measure that is still being debated. It is the factories that are shuttered.
The last thing this is about is the Dow.
it is ridiculous to equate this with the DOW when regular people with no investments are the ones the most severely affected
You could make this argument about any potentially fatal disease. Are you willing to crash the economy to save the people who die of the flu every year? Probably not, right? Presumably because fewer people die of the flu.
It's an unpleasant reality, but it's a question of degree--how many lives are worth saving the world economy for everyone else? If your answer is "zero", then why should we not apply that standard to every other disease?
I'm more interested in how you calculated those numbers. Could you share? Thank you for your concern about my empathy.
Yes
I want to agree with you, but I do have a concern about this point of view that I've yet to see addressed. Perhaps you or someone else can give some insight into your thinking.
What about the deaths caused by the wrecked economy?
Even putting things in simplistic terms, the 2008 crash is credited with 10,000 suicides. Following the chain of misery into homelessness, stress-induced illnesses, criminality (which leads to loss of life in multiple categories), and so forth leads me to question whether it is actually as black and white as people say it is.
Currently, we're on track to make this economic catastrophe bigger than the 2008 crash. I don't want to imagine what the fallout is going to look like in the future, but I already know a lot of people who are out of work and scared.
Mind you, I'm saying this as someone who has family in the 70+ high-risk category. I'm not oblivious to the value of human life. I am purely speculating about whether or not we're actually doing the right thing, and thinking about the consequences as thoroughly as we should.
The theory that the virus has been spreading for months just makes no sense at all. By all accounts, COVID has a pretty quick progression and serious cases need to be hospitalized after about one week of symptoms or about two weeks after contraction. How come that the virus has infected millions and millions of people in the last months but none of them got seriously sick. And now all of a sudden, 1000s are dying. Unfortunatly, it seems that even researchers from reputable institutions are now just pulling numbers from their asses to grab some headlines.
Maybe they were dying before, but were categorized differently?
"He said that expected increases in National Health Service capacity and ongoing restrictions to people’s movements make him “reasonably confident” the health service can cope when the predicted peak of the epidemic arrives in two or three weeks. UK deaths from the disease are now unlikely to exceed 20,000, he said, and could be much lower."[1]
So basically "Scientist revises model based on new conditions". Isn't that supposed to happen?
A successful prevention is going to feel like failure. It's going to prompt questions like "was this worth all the panic, and tanking the economy?" Bodies are easy to count, deaths prevented are invisible.
1. https://www.newscientist.com/article/2238578-uk-has-enough-i...
Not quite. You're claiming that this is all because of the lockdown, when it is quite clear that the biggest reason for the change is the changed estimate of R0.
In other words, if this new R0 estimate is correct we were completely mislead about how big a deal this virus is, and comparisons to it "just" being like a bad flu year are more or less correct.
> and comparisons to it "just" being like a bad flu year are more or less correct.
We've never had to build temporary hospitals to house 4,000 patients before, even in bad flu years. It's more infectious than flu and it hospitalises more people than flu. People keep talking about the death rate: there are other important things. How many people does it hospitalise? What happens to the people who can't get hospital treatment if the hospitals are full?
From places like Spain and Italy we know it puts a lot of people in hospital, and we know when that happens it starts shifting the mortality from the old people who were going to die anyway to younger people.
It's not like flu.
https://twitter.com/iamyourgasman/status/1241267189048578048
But it seems like he's revising the R0 estimate upwards, rather than downwards. From the same article:
"New data from the rest of Europe suggests that the outbreak is running faster than expected, said Ferguson. As a result, epidemiologists have revised their estimate of the reproduction number (R0) of the virus. This measure of how many other people a carrier usually infects is now believed to be just over three, he said, up from 2.5. “That adds more evidence to support the more intensive social distancing measures,” he said."
https://www.newscientist.com/article/2238578-uk-has-enough-i...
Yeah, revising the R0 upwards is exactly what you would expect to result in a faster infection rate. This means that more people have already been infected and were fine, and that we are at the peak before herd immunity starts to drastically impact the R0 downwards.
Exactly right. If this new R0 estimate is correct then I'm not going to be pleased AT ALL with anyone saying: "We did it, the lockdown worked! By the way, ignore the extra suicides and child abuse this year."
I also believe the referenced article does not correctly convey what the original New Scientist article does: the original epidemiologist does not buy in completely to the new inferences, by my reading.
He tweeted some clarification.
https://twitter.com/neil_ferguson/status/1243294815200124928
Another model that fits the UK data, even when assuming that half of the population has already been infected, which would mean that the disease is not nearly as dangerous as commonly thought: https://nymag.com/intelligencer/2020/03/oxford-study-coronav...
By adjusting those two parameters (R0 and IFR) in opposite directions, you can come up with a whole gamut of scenarios that match the evidence pretty well.
Sure, that model shows that there are multiple ways to fit the model parameters to account for the first 14 days of fatalities. But we have a lot more real-world data available that directly contradicts any scenario where half the population is already infected.
I'm just disgusted that the authors are now saying it was just an abstract demonstration of different scenarios, and pretending they didn't actually make the claims about the real world that they did.
...is all of that real-world data from Lombardy? If not, does it rely on circular logic? I’ve seen evidence in both categories, but not much else.
Sure, here's some to start with.
1. Any situation where the testing isn't limited to just the most critically ill showing much lower infection percentages than this model would predict. A lot of these are tests of every person in a risky situation, whether they had symptoms or not:
E.g. the evacuation flights from Iran to China tested every passenger and showed a 3% infection rate. T The village of Vo testing their entire population twice soon after starting a Covid quarantine, with a 3% infection rate.
Others were testing large amounts of people with no particular reason they had Covid, but still with a skewed sample:
The Swedish sentinel testing of random people with any kind of flu symptoms (1.5% of people with flu symptoms testing as positive for Covid, vs. 30% testing positive for Influenza A/B). Iceland testing IIRC a volunteer 1.5% of their population whether they had symptoms or not, and having something like a 1% infection rate.
The thing all of these have in common is that they happened at a time in their relative epidemics where this model should have predicted the majority of the population was currently infected.
In fact, it's basically impossible to explain any testing results, since even when they're biased to cases where Covid is strongly suspected, the ratio of positives is so low. If we test the 1000 of people most suspected of having Covid right now, and get 10% positives, how can it possibly be the case that half the non-suspicious population are carriers at the time of that test?
2. If herd immunity kicks in 14 days after the first death as implied by this model, why haven't any of the epidemics died down by now? Italy is on what, day 30?
3. How does a super infectious but low mortality model explain the geographic clustering of deaths? Sure, the geographic clustering of known cases could be explained by testing bias. But deaths don't have that bias.
4. Observed high CFRs in limited populations where we know the infection rate was high. E.g. Diamond Princess at what 1.4%, and still another 2% in critical condition. How many top ranking Iranian leaders died in short order of Covid, and how does that fit in with a mortality rate of 0.01%? Or the cases where most of the patients of a health care facility or nursing home got infected?
1. Unless I'm mistaken, this (extreme scenario in the) Oxford model does not say that 50% of the population is infected all at the same time. Until there are serological surveys that indicate who was infected in the past, the current tests can't prove what you think. And any false negatives from the PCR test would add to this problem.
2. The progress of the disease takes time too, from infection to symptoms to detection/hospitalization/secondary infection. And efforts to "flatten the curve" will "slow the spread" too.
3. I'm not sure about this one, but shouldn't the infectiousness also vary quite a lot with different contributing factors (population density, air quality, etc)? The number used in the model is always just an "average" in a sense.
4. The cruise ship evidence is pretty significant, but it still has problems. Any passengers who had already recovered or did not show symptoms could have been missed. Plus I can name several factors in that situation that might increase mortality off the top of my head. It's not the best sample for drawing conclusions about people who are on average less old, not traveling, and so forth.
Admittedly, the extreme "50% infected" scenario has a risk factor (same as IFR?) of 0.001%, which my gut feeling says is too optimistic. But as far as I know none of the scenarios can be conclusively disproven (until they can do proper serological surveys).
1. They have the virus go from like 5% infected to 70% in two weeks in that scenario. Given what we know about the incubation period and they assume about the infectious period, more than half the population has to be infected (and probably even infectious) somewhere along that curve.
2. Their model predicts that the peak of the epidemic in Italy should have been before March 5th (first death on February 22nd + 14 days, at which point easily more than half the population is infected). There should have been a sharp drop in new cases about a week later, as the virus burnt itself down. But here we are three weeks later, and it's still not entirely clear that the peak has been found.
Italy did not institute significant nation-wide measures until March 9th, so the "slowdown from measures" explanation makes no sense.
3. Agreed. But their entire model is predicated on treating the entire country as a single unit. That's probably a part of the reason why the results are so absurd. I don't think it's fair to excuse the model for regional differences, but require any criticism of the model to take them into account.
4. The difference between the model's prediction and apparent reality is likely to be about a factor of 200. Even assuming everybody on the ship was actually infected, that only cuts it to a factor of 40 difference.
And it's really not just that single case. Consider that infamous Washington state nursing home. 120 residents, 35 dead from Covid to date. Even if we assume that every single one of the 120 was infected despite not testing positive, that still an IFR of 30%. Sure, it's a high-risk segment. But it's also a large enough segment a 30% IFT for them makes it quite impossible for the population-wide IFR to be 0.01%.
(Re: your last point, they had two parameters. One for being at risk of becoming a serious case, and another of dying if serious. The two need to be multiplied to get their predicted IFR. They assumed that 0.1% of population were at risk to become severe cases, and 15% of the severe cases died. So about 0.01%).
So on the one hand, I think you're bringing up enough problems for me to agree that the extreme scenario is not likely to hold up.
On the other, I don't understand how after making so many (often reasonable) assumptions in your arguments, you can say that it's "quite impossible" for the total infected to be so high, or the population-wide IFR to be three orders of magnitude lower than a nursing home or any other special case that you do not fully understand.
Here are some of those assumptions, which again do not all seem unreasonable to me:
- A mild case would likely be detectable by a PCR test for 8+ days
- The PCR test does not have a high false-negative rate in mild cases (see https://www.researchsquare.com/article/rs-17319/v1)
- Italy has not already had a sharp drop in new infections/most new infections are being identified as cases within two weeks
- Italy did nothing to slow the rate of infection until the full lockdown was in effect (but slower spread would mean higher mortality, no matter the reason)
- COVID-19 was the only/main thing that contributed to mortality in the special cases
The Oxford model is pretty weak though, isn't it? It makes a bunch of assumptions that are different from the data we already have from other countries. It ignores situations like Diamond Princess. If the Oxford model is true it means the death rate is absurdly low.
Has it even been peer reviewed yet?
Bill Gates on the Imperial College model: 'Fortunately it appears the parameters used in that model were too negative. ... Models are only as good as the assumptions put into them' https://www.reddit.com/r/Coronavirus/comments/fksnbf/im_bill...
That's just fine and dandy then, I didn't lose my job, and Bill Gates doesn't need to earn money.
Un-fucking-believable.
This is flat out wrong. There is no revision at all. To quote [Neil Ferguson himself](https://twitter.com/neil_ferguson/status/1243294815200124928):
I think it would be helpful if I cleared up some confusion that has emerged in recent days. Some have interpreted my evidence to a UK parliamentary committee as indicating we have substantially revised our assessments of the potential mortality impact of COVID-19
This is not the case. Indeed, if anything, our latest estimates suggest that the virus is slightly more transmissible than we previously thought. Our lethality estimates remain unchanged.
My evidence to Parliament referred to the deaths we assess might occur in the UK in the presence of the very intensive social distancing and other public health interventions now in place.
Without those controls, our assessment remains that the UK would see the scale of deaths reported in our study (namely, up to approximately 500 thousand).
Made me thinking - Is this the reason behind today's market's rally close to the end of the day?
I hope he’s right.
So we are normalizing rightwing propaganda sites on HN now? Seeing an article from a Ben Shapiro fronted site on the HN front page is frightening. I need a new place to get my information.
If you would prefer to ignore the fact that the hugely influential epidemiologists responsible for the recommendations we are currently following have changed their recommendations, go for it.
We can all get our information directly from the source: https://www.newscientist.com/article/2238578-uk-has-enough-i...
how is a massive change in the predicted death rate used to justify the current measures propaganda?
the original newscientist article buries the lede near the end
all shapiro's blog does is move the lede to the beginning and add contexr
does hn avoid normalizing leftwing propaganda? if not, why make a special effort for other propaganda sources?